AI HiL Architect - Software Development (Loma Bonita)

Job Overview: AI Architect
- Software Development The AI Architect will define the technical vision and structural framework for Ford’s AI-driven automation initiatives. You will be responsible for designing scalable, secure, and high-performance AI architectures that integrate into the general V&V; (Verification & Validation) ecosystem. Your goal is to ensure that individual AI tools work together as a cohesive, enterprise-grade platform that enables rapid software delivery. Key Responsibilities 4 Days On Site: GTBC Ford México (Naucalpan, México) Architectural Design: Define the end-to-end architecture for AI solutions, including data pipelines, model training environments, and deployment strategies (MLOps). Technology Roadmap: Evaluate and select the tech stack (e.g., LLMs, Computer Vision frameworks, Vector Databases) that will support the goal of a 2-week testing cycle. MLOps & Governance: Establish standards for model versioning, monitoring, and "AI Ethics" to ensure that automated validations are reliable, repeatable, and compliant. Scalability & Integration: Ensure AI solutions are modular and can be integrated across different vehicle platforms and software domains without starting from scratch. Technical Leadership: Act as a mentor to AI Developers, conduct code/architecture reviews, and troubleshoot complex system-level bottlenecks. Cross-Functional Alignment: Collaborate with IT, Security, and Cloud Infrastructure teams to ensure the AI platform is robust and meets Ford’s enterprise standards. Technical Skills & Qualifications System Design: Proven experience in designing microservices architectures and distributed systems. Advanced AI/ML: Deep understanding of Deep Learning, Natural Language Processing (NLP), and Generative AI (LLMs) architecture. MLOps Mastery: Experience with tools like Kubeflow, MLflow, or SageMaker to automate the lifecycle of AI models. Cloud Infrastructure: Expert knowledge of Azure or GCP, specifically regarding GPU provisioning, containerization (Docker/Kubernetes), and serverless computing. Data Strategy: Ability to design data lakes and warehouses that provide the "fuel" for AI automation. Experience: Typically 5+ years in software engineering, with at least 3 years in a lead or architectural role focused on AI/ML.

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